Set Options

knitr::opts_chunk$set(
  warning = TRUE, # show warnings during codebook generation
  message = TRUE, # show messages during codebook generation
  error = TRUE, # do not interrupt codebook generation in case of errors,
                # usually better for debugging
  inclue = TRUE,
  echo = TRUE  # show R code
)
ggplot2::theme_set(ggplot2::theme_bw())

library(codebook)
library(rio)
library(labelled)

## 
## Attaching package: 'labelled'

## The following object is masked from 'package:codebook':
## 
##     to_factor

library(flextable)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(stringr)

Prep Data

lab_info <- dir(path = "..", full.names = TRUE, recursive = TRUE, 
    include.dirs = TRUE, pattern = "Lab_Info.csv") %>%
 import()

# Load raw SPV data 
# Build data frame of valid SP verification responses
all_files <- list.files(path = "../data",
                         pattern = ".csv", 
                         full.names = TRUE,
                         recursive = TRUE)
all_files <- all_files[grepl("PP|SP|jatos_results|pp|sp|Pp|Sp", all_files)]

all_list <- list()
for (i in 1:length(all_files)){
  all_list[[i]] <- import(all_files[i])
  all_list[[i]]$unique_id <- all_files[i]
  
  all_list[[i]] <- all_list[[i]] %>% 
    mutate(across(everything(), as.character))
}

all_data <- bind_rows(all_list) %>% 
  select(subject_nr, 
         LAB_SEED, 
         datetime, 
         logfile, 
         task_order,
         List, 
         PList,
         Match,
         Orientation, 
         Probe,
         Target,
         response_time, 
         correct,
         PPList, 
         Orientation1,
         Orientation2, 
         Identical,
         Picture1,
         Picture2, 
         opensesame_codename,
         opensesame_version,
         unique_id) %>% 
  filter(!is.na(opensesame_codename))

all_data$PSA_ID <- substr(all_data$unique_id, 9, 16)
all_data$PSA_ID <- gsub("\\/", "", all_data$PSA_ID)

all_data$subject_nr[na.omit(all_data$opensesame_codename != "osweb")] <- 
  gsub("../data/|SP|PP|_|-| |.csv", "", all_data$unique_id[na.omit(all_data$opensesame_codename != "osweb")])

SP_V <- all_data %>% select(
  PSA_ID, subject_nr, LAB_SEED, datetime, logfile, task_order, List,
  Match, Orientation, PList, Probe, Target, response_time, correct, 
  opensesame_codename, opensesame_version) %>% 
  left_join(lab_info %>% select(PSA_ID, Language), by = "PSA_ID") %>% 
  readr::type_convert() %>% 
  distinct() %>% 
  mutate(Language = ifelse(Language == "Magyar", "Hungarian", Language)) %>% ## Switch "Magyar" to "Hungarian"
 mutate(Language = ifelse(Language == "Simple Chinese", "Simplified Chinese", Language)) %>% ## Switch "Simple Chinese" to "Simplified Chinese"
 mutate(PSA_ID = str_replace(PSA_ID, "SRB_002B", "SRB_002")) %>% ## Combine two Serbian language groups based on the collectors' recommendation
 mutate(Source = if_else(opensesame_codename == "osweb","osweb","site"), 
  Subject = paste0(Source,"_",PSA_ID,"_",subject_nr, "_", datetime)) %>% ## Compose the unique participant id
 subset(Match != "F") ## Exclude fillers in SP_V

## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   PSA_ID = col_character(),
##   subject_nr = col_character(),
##   LAB_SEED = col_character(),
##   datetime = col_character(),
##   logfile = col_character(),
##   task_order = col_character(),
##   List = col_character(),
##   Match = col_character(),
##   Orientation = col_character(),
##   PList = col_character(),
##   Probe = col_character(),
##   Target = col_character(),
##   response_time = col_double(),
##   correct = col_double(),
##   opensesame_codename = col_character(),
##   opensesame_version = col_character(),
##   Language = col_character()
## )

# Build data frame of valid PP responses
PP <- all_data %>% select(
  PSA_ID, LAB_SEED, datetime, logfile, subject_nr, PPList, 
  Orientation1, Orientation2, Identical, Picture1, Picture2, 
  response_time, correct, opensesame_codename, opensesame_version) %>% 
  left_join(lab_info %>% select(PSA_ID, Language), by = "PSA_ID") %>% 
  readr::type_convert() %>% 
  distinct() %>% 
  mutate(Language = ifelse(Language == "Magyar", "Hungarian", Language)) %>% ## Switch "Magyar" to "Hungarian"
  mutate(Language = ifelse(Language == "Simple Chinese", "Simplified Chinese", Language)) %>% ## Switch "Simple Chinese" to "Simplified Chinese"
  mutate(PSA_ID = str_replace(PSA_ID, "SRB_002B", "SRB_002")) %>% ## Combine two Serbian language groups based on the collectors' recommendation
  mutate(Source = if_else(opensesame_codename == "osweb","osweb","site"), 
  Subject = paste0(Source,"_",PSA_ID,"_",subject_nr, "_", datetime)) %>%  ## Compose the unique participant id
  filter(Identical != "F") # practice trials

## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   PSA_ID = col_character(),
##   LAB_SEED = col_character(),
##   datetime = col_character(),
##   logfile = col_character(),
##   subject_nr = col_character(),
##   PPList = col_character(),
##   Orientation1 = col_character(),
##   Orientation2 = col_character(),
##   Identical = col_character(),
##   Picture1 = col_character(),
##   Picture2 = col_character(),
##   response_time = col_double(),
##   correct = col_double(),
##   opensesame_codename = col_character(),
##   opensesame_version = col_character(),
##   Language = col_character()
## )

SP_V$Subject[SP_V$opensesame_codename != "osweb"] <-
  paste0(SP_V$Source[SP_V$opensesame_codename != "osweb"],
         "_",
         SP_V$PSA_ID[SP_V$opensesame_codename != "osweb"],
         "_",
         SP_V$subject_nr[SP_V$opensesame_codename != "osweb"])

PP$Subject[PP$opensesame_codename != "osweb"] <- paste0(PP$Source[PP$opensesame_codename != "osweb"],"_",PP$PSA_ID[PP$opensesame_codename != "osweb"],"_",PP$subject_nr[PP$opensesame_codename != "osweb"])

PP$Subject[PP$Subject == "site_MYS_003_MYS003/MYS00324"] <- rep(c("site_MYS_003_MYS003/MYS00324", "site_MYS_003_MYS003/MYS00324_1"), each = 24)

SP_V$Subject[SP_V$Subject == "site_USA_173_USA173/USA17328
"] <- c(rep("site_USA_173_USA173_28", 30), rep("site_USA_173_USA173/USA17328_1", 29))

# fix issues with duplicate trials 
SP_V <- SP_V %>% 
  filter(!duplicated(SP_V %>% select(Subject, Target)))
PP <- PP %>% 
  filter(!duplicated(PP %>% select(Subject, Picture1)))

# test subjects counts
SP_V_subjects <- SP_V %>% 
  group_by(Subject, PSA_ID) %>% 
  count() 

PP_subjects <- PP %>% 
  group_by(Subject, PSA_ID) %>% 
  count()

merge_subjects <- SP_V_subjects %>% 
  full_join(PP_subjects, by = "Subject") %>% 
  rename(SP_Data = n.x,
         PP_Data = n.y)

merge_subjects$PSA_ID.x[is.na(merge_subjects$PSA_ID.x)] <- merge_subjects$PSA_ID.y[is.na(merge_subjects$PSA_ID.x)]

merge_subjects$PSA_ID <- merge_subjects$PSA_ID.x

merge_subjects <- merge_subjects %>% select(-PSA_ID.x, -PSA_ID.y)

osweb_meta <- dir(path = ".."
                  ,full.names = TRUE, recursive = TRUE, 
                  include.dirs = TRUE, pattern = "jatos_meta.csv") %>%
  import() %>%
  unique() %>% 
  mutate(Batch = str_replace(Batch, "SRB_002B", "SRB_002")) %>% 
  mutate(gender = ifelse(gender==1,"FEMALE",ifelse(gender==2,"MALE","MISSING"))) %>%
  # mutate(birth_year_tr = as.numeric(birth_year)) %>%
  mutate(birth_year_tr = as.numeric(gsub(birth_year,pattern="NA|x",replacement = ""))) %>%
  mutate(year = ifelse(birth_year_tr > 21 & !is.na(birth_year_tr), 1900 + birth_year_tr, 2000 + birth_year_tr)) %>%
  mutate(age = ifelse(!is.na(year),2021-year,NA)) %>%
  group_by(Batch) %>%
  summarise(N = n(), 
            Female_N = sum(gender=="FEMALE",na.rm = TRUE), 
            Male_N = sum(gender=="MALE", na.rm = T), 
            Age = mean(age, na.rm=TRUE), 
            AgeSD = sd(age, na.rm=TRUE), 
            Proficiency = mean(lang_prof),
            missing_age = sum(is.na(age)))

site_meta <- import("includes/files/insite_meta.csv") %>% 
  mutate(gender = ifelse(gender=="female","FEMALE",ifelse(gender=="male","MALE","MISSING"))) %>%
  unique() %>% 
  group_by(PSA_ID) %>% 
  summarize(N = n(), Female_N = sum(gender=="FEMALE", na.rm = TRUE), 
            Male_N = sum(gender=="MALE", na.rm = T), 
            Age = mean(age, na.rm=TRUE),
            AgeSD = sd(age, na.rm=TRUE), 
            missing_age = sum(is.na(age))
            )

merge_subjects <- merge_subjects %>% 
  group_by(PSA_ID) %>% 
  summarize(SP_N_trials = sum(SP_Data, na.rm = T),
            PP_N_trials = sum(PP_Data, na.rm = T),
            SP_N = length(na.omit(SP_Data)),
            PP_N = length(na.omit(PP_Data))) %>% 
  full_join(site_meta, by = "PSA_ID") %>% 
  full_join(osweb_meta, by = c("PSA_ID" = "Batch"))

merge_subjects <- merge_subjects %>% 
  group_by(PSA_ID) %>% 
  mutate(Overall_N_Gender = sum(c(N.x, N.y), na.rm = T),
         Overall_N_Female = sum(c(Female_N.x, Female_N.y), na.rm = T),
         Overall_N_Male = sum(c(Male_N.x, Male_N.y), na.rm = T),
         Overall_Age = mean(c(Age.x, Age.y), na.rm = T), 
         Overall_SD = mean(c(AgeSD.x, AgeSD.y), na.rm = T),
         Overall_Missing_Age = sum(c(missing_age.x, missing_age.y), na.rm = T)) %>% 
  left_join(lab_info %>% select(PSA_ID, Language) %>% unique(), by = "PSA_ID")

flextable(merge_subjects %>% 
            select(1:5, 19:25)) %>% 
  set_header_labels(c("Lab ID Code", "SP Number Trials", "PP Number Trials", 
                      "SP Participants", "PP Participants", "Demographic Sample Size", 
                      "Female Sample Size", "Male Sample Size", "Average Age", 
                      "SD Age", "Missing Age", "Language")) %>% 
  set_caption(caption = "Sample Size and Meta Data Information")

Sample Size and Meta Data Information
PSA_ID	SP_N_trials	PP_N_trials	SP_N	PP_N	Overall_N_Gender	Overall_N_Female	Overall_N_Male	Overall_Age	Overall_SD	Overall_Missing_Age	Language
ARE_001	1,248	1,248	52	52	53	0	0	38.00000		52	Arabic
ARE_002	1,296	1,296	54	54	54	42	12	26.51020	18.5877675	5	Arabic
AUS_002	2,376	2,376	99	99	103	46	37	20.14141	3.3228034	4	English
AUS_091	3,840	3,840	160	160	160	127	25	26.03185	11.5466836	3	English
AUT_002	2,400	2,400	100	100	114	0	1	20.94175	2.5622648	11	German
AUT_005	2,592	2,592	108	108	108	80	22	22.17925	4.2601077	2	German
BRA_003	1,200	1,200	50	50	50	36	13	30.80000	8.7341694	0	Brazilian Portuguese
CAN_020	2,352	2,376	98	99	104	54	40	20.25510	3.6589425	6	English
CHN_005	1,200	1,200	50	50	57	0	0	18.66038	3.9171389	4	Simple Chinese
CHN_019	840	816	35	34	39	0	1	25.17143	5.4367254	4	Simple Chinese
COL_001	1,680	1,656	70	69	70	0	0	21.35714	3.3623478	0	Spanish
DEU_020	624	624	26	26	26	18	3	23.88462	3.3861710	0	German
ECU_001	1,440	1,440	60	60	76	0	0	22.09722	4.2992293	4	Spanish
GBR_005	1,272	1,272	53	53	76	57	13	19.95890	3.8960035	3	English
GBR_006	1,200	1,200	50	50	51	37	13	20.14000	2.4578903	1	English
GBR_014	1,200	1,200	50	50	58	46	11	18.73684	1.6204752	1	English
GBR_043	720	720	30	30	32	15	11	25.70000	9.3960740	2	English
GRC_002	2,376	2,376	99	99	109	0	0	33.85981	11.3040722	2	Greek
HUN_001	3,610	3,816	151	159	168	3	1	21.50299	2.8153500	1	Magyar
IND_003	1,896	1,896	79	79	86	57	27	21.66265	3.4580310	3	Hindi
ISR_001	3,576	3,571	149	149	181	0	0	24.25309	9.2898875	19	Hebrew
MYS_003	1,200	1,248	50	52	52	38	11	22.56250	3.8971143	4	English
MYS_004	2,400	2,400	100	100	109	65	30	20.73148	2.0028320	1	English
NGA_001	1,248	1,248	52	52	52	24	22	23.94231	11.2901370	0	English
NOR_002	504	504	21	21	21	12	8	30.09524	8.5784892	0	Norwegian
NOR_003	1,320	1,320	55	55	53	1	1	23.55102	6.2452022	4	Norwegian
NOR_004	1,752	1,752	73	73	80	0	0	22.00000	4.3841509	2	Norwegian
NZL_005	7,680	7,680	320	320	320	244	56	23.21311	5.4287089	15	English
POL_001	1,368	1,368	57	57	146	0	0	23.24615	7.9553614	16	Polish
POR_001	1,488	1,464	62	61	55	26	23	30.74074	9.0890462	1	Portuguese
PSA_001	1,248	1,272	52	53	71	50	12	18.89231	0.9539896	6	English
PSA_002	1,536	1,536	64	64	102	79	11	19.82488	2.4172552	5	English
SRB_002	3,120	3,120	130	130	130	108	21	21.37500	4.5002187	2	Serbian
SVK_001	2,419	2,400	101	100	103	1	0	21.58824	2.5147208	1	Slovak
SVK_002	1,462	1,199	61	50	222	0	0	21.96172	2.1390304	13	Slovak
THA_001	1,200	1,152	50	48	50	29	9	21.54000	3.8130282	0	Thai
TUR_007	2,184	2,184	91	91	93	0	0	20.92308	2.9334499	2	Turkish
TUR_007E	264	264	11	11	12	9	2	20.36364	1.9116865	1	English
TUR_023	1,896	1,896	79	79	80	36	14	21.57895	8.6379822	3	Turkish
TUR_025	2,376	2,352	99	98	101	0	0	21.63000	2.1911438	1	Turkish
TWN_001	1,440	1,440	60	60	70	45	14	20.72727	1.2095194	4	Traditional Chinese
TWN_002	2,160	2,160	90	90	116	24	32	21.04347	3.6608571	17	Traditional Chinese
TWN_002E	288	288	12	12	12	6	5	21.16667	1.1934163	0	English
USA_011	1,512	1,512	63	63	63	30	23	22.34426	11.5453963	2	English
USA_020	7,980	8,064	333	336	403	258	76	19.63290	2.1214349	63	English
USA_030	648	648	27	27	31	20	3	36.00000	0.9607689	3	English
USA_032	1,209	1,224	51	51	51	30	21	19.29412	1.5138576	0	English
USA_033	3,000	3,024	125	126	129	90	25	20.05952	1.3554741	10	English
USA_065	1,200	1,200	50	50	61	35	15	18.85714	1.6340532	5	English
USA_173	792	744	33	31	3	0	3	19.66667	0.5773503	0	English
					1	0	0			1
0000					3	0	1	19.00000		2
040					1	0	0			1
1					2	0	0	100.00000		1
11					2	0	0			2
123					1	0	0			1
1234					1	0	0			1
149					1	0	0	20.00000		0
173					1	0	1	22.00000		0
19					1	0	0			1
23232					1	0	0			1
26844					1	0	0	22.00000		0
3726					6	0	0	33.00000		5
54					1	0	0	23.00000		0
75AB					1	0	0			1
87					1	0	0	29.00000		0
9317					2	0	0			2
94					1	0	0	23.00000		0
9951					1	0	1	22.00000		0
BRA_004					1	0	0			1	Portuguese
MAC_001					1	0	0			1
MYS_033					1	1	0	19.00000		0
POL_003					1	0	0			1
SKV_002					1	0	0	22.00000		0
USA_002					1	0	0			1
USA_003					3	2	1	19.33333	0.5773503	0
jnkjn					1	0	0			1

codebook_data <- bind_rows(SP_V, PP)

var_label(codebook_data) <- list(
  PSA_ID = "Lab identification code",
  subject_nr = "Original, lab assigned subject number",
  LAB_SEED = "Original, lab assigned seed number for randomization",
  datetime = "Date and time of the study", 
  logfile = "Original location of the saved log",
  task_order = "", 
  List = "List file for the presentation of the stimuli",
  Match = "Match or Mismatch of the sentence and picture for sentence picture trials. F indicates practical trials.",
  Orientation = "Direction of the stimuli picture presented on the screen", 
  PList = "List file for the practice stimuli presentation",
  Probe = "Sentence seen in the sentence picture matching task",
  Target = "Object seen in the sentence picture matching task", 
  response_time = "Response time to determine if objects or sentence/picture matched",
  correct = "Indicates if the participant answered the trial correctly",
  opensesame_codename = "Name of the version of open sesame",
  opensesame_version = "Version number of the open sesame used",
  Language = "Language the participant took the study in",
  Source = "Online (osweb) versus in person (all others) data source",
  Subject = "A unique participant identifier, as duplicates and other repeating trials were fixed", 
  PPList = "The stimulus presentation list for picture picture matching task",
  Orientation1 = "The orientation of the first picture in the picture picture matching task",
  Orientation2 = "The orientation of the second picture in the picture picture matching task",
  Identical = "If the two orientations of the pictures matched in the picture picture matching task",
  Picture1 = "Name of the first picture in the picture picture matching task",
  Picture2 = "Name of the secon picture in the picture picture matching task"
  )

metadata(codebook_data)$name <- "Object Orientation across languages"
metadata(codebook_data)$description <- "This dataset includes the raw trial data from the PSA002: Object Orientation Across Languages Study. 

Mental simulation theories of language comprehension propose that people automatically create mental representations of objects mentioned in sentences. Representation is often measured with the sentence-picture verification task, in which participants first read a sentence implying the shape/size/color/object orientation and, on the following screen, a picture of an object. Participants then verify if the pictured object either matched or mismatched the implied visual information mentioned in the sentence. Previous studies indicated the match advantages of shapes, but findings concerning object orientation were mixed across languages. This registered report describes our investigation of the match advantage of object orientation across 18 languages, which was undertaken by multiple laboratories and organized by the Psychological Science Accelerator. The preregistered analysis revealed that there is no compelling evidence for a global match advantage, although some evidence of a match advantage in one language was found. Additionally, the match advantage was not predicted by mental rotation scores which does not support current embodied cognition theories."
metadata(codebook_data)$identifier <- "https://osf.io/e428p/"
metadata(codebook_data)$creator <- "Erin M. Buchanan"
metadata(codebook_data)$citation <- "Chen et al. (2023). Investigating Object Orientation Effects Across 18 Languages. Registered Report."
metadata(codebook_data)$url <- "https://osf.io/e428p/"
metadata(codebook_data)$datePublished <- "2023-02-10"
metadata(codebook_data)$temporalCoverage <- "2019-2021" 
metadata(codebook_data)$spatialCoverage <- "Online and in Person"

Create codebook

codebook(codebook_data)

Metadata

Description

Dataset name: Object Orientation across languages

This dataset includes the raw trial data from the PSA002: Object Orientation Across Languages Study.

Mental simulation theories of language comprehension propose that people automatically create mental representations of objects mentioned in sentences. Representation is often measured with the sentence-picture verification task, in which participants first read a sentence implying the shape/size/color/object orientation and, on the following screen, a picture of an object. Participants then verify if the pictured object either matched or mismatched the implied visual information mentioned in the sentence. Previous studies indicated the match advantages of shapes, but findings concerning object orientation were mixed across languages. This registered report describes our investigation of the match advantage of object orientation across 18 languages, which was undertaken by multiple laboratories and organized by the Psychological Science Accelerator. The preregistered analysis revealed that there is no compelling evidence for a global match advantage, although some evidence of a match advantage in one language was found. Additionally, the match advantage was not predicted by mental rotation scores which does not support current embodied cognition theories.

Metadata for search engines

Temporal Coverage: 2019-2021
Spatial Coverage: Online and in Person
Citation: Chen et al. (2023). Investigating Object Orientation Effects Across 18 Languages. Registered Report.
URL: https://osf.io/e428p/
Identifier: https://osf.io/e428p/
Date published: 2023-02-10
Creator:

name	value
1	Erin M. Buchanan

x
PSA_ID
subject_nr
LAB_SEED
datetime
logfile
task_order
List
Match
Orientation
PList
Probe
Target
response_time
correct
opensesame_codename
opensesame_version
Language
Source
Subject
PPList
Orientation1
Orientation2
Identical
Picture1
Picture2

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
logfile	Original location of the saved log	character	67416	0.6481603	5142	0	8	105	0

task_order

Distribution

Distribution of values for task_order

129498 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
task_order		character	129498	0.3241584	3	0	2	13	0

List

List file for the presentation of the stimuli

Distribution

Distribution of values for List

95778 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
List	List file for the presentation of the stimuli	character	95778	0.5001409	68	0	1	15	0

Match

Match or Mismatch of the sentence and picture for sentence picture trials. F indicates practical trials.

Distribution

Distribution of values for Match

95778 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
Match	Match or Mismatch of the sentence and picture for sentence picture trials. F indicates practical trials.	character	95778	0.5001409	2	0	1	1	0

Orientation

Direction of the stimuli picture presented on the screen

Distribution

Distribution of values for Orientation

95778 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
Orientation	Direction of the stimuli picture presented on the screen	character	95778	0.5001409	2	0	1	1	0

PList

List file for the practice stimuli presentation

Distribution

Distribution of values for PList

101514 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
PList	List file for the practice stimuli presentation	character	101514	0.4702051	34	0	1	14	0

Probe

Sentence seen in the sentence picture matching task

Distribution

Distribution of values for Probe

95778 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
Probe	Sentence seen in the sentence picture matching task	character	95778	0.5001409	1096	0	11	132	0

Target

Object seen in the sentence picture matching task

Distribution

Distribution of values for Target

95778 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
Target	Object seen in the sentence picture matching task	character	95778	0.5001409	48	0	8	22	0

response_time

Response time to determine if objects or sentence/picture matched

Distribution

Distribution of values for response_time

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
response_time	Response time to determine if objects or sentence/picture matched	numeric	0	1	0	609	472186	815.4908	2870.269	▇▁▁▁▁

correct

Indicates if the participant answered the trial correctly

Distribution

Distribution of values for correct

1 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	min	median	max	mean	sd	hist
correct	Indicates if the participant answered the trial correctly	numeric	1	0.9999948	0	1	1	0.9289334	0.2569367	▁▁▁▁▇

opensesame_codename

Name of the version of open sesame

Distribution

Distribution of values for opensesame_codename

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
opensesame_codename	Name of the version of open sesame	character	0	1	3	0	5	17	0

opensesame_version

Version number of the open sesame used

Distribution

Distribution of values for opensesame_version

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
opensesame_version	Version number of the open sesame used	character	0	1	7	0	5	10	0

Language

Language the participant took the study in

Distribution

Distribution of values for Language

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
Language	Language the participant took the study in	character	0	1	18	0	4	20	0

Source

Online (osweb) versus in person (all others) data source

Distribution

Distribution of values for Source

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
Source	Online (osweb) versus in person (all others) data source	character	0	1	2	0	4	5	0

Subject

A unique participant identifier, as duplicates and other repeating trials were fixed

Distribution

Distribution of values for Subject

0 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
Subject	A unique participant identifier, as duplicates and other repeating trials were fixed	character	0	1	4248	0	25	87	0

PPList

The stimulus presentation list for picture picture matching task

Distribution

Distribution of values for PPList

95832 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
PPList	The stimulus presentation list for picture picture matching task	character	95832	0.4998591	8	0	1	12	0

Orientation1

The orientation of the first picture in the picture picture matching task

Distribution

Distribution of values for Orientation1

95832 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
Orientation1	The orientation of the first picture in the picture picture matching task	character	95832	0.4998591	2	0	1	1	0

Orientation2

The orientation of the second picture in the picture picture matching task

Distribution

Distribution of values for Orientation2

95832 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
Orientation2	The orientation of the second picture in the picture picture matching task	character	95832	0.4998591	2	0	1	1	0

Identical

If the two orientations of the pictures matched in the picture picture matching task

Distribution

Distribution of values for Identical

95832 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
Identical	If the two orientations of the pictures matched in the picture picture matching task	character	95832	0.4998591	2	0	1	1	0

Picture1

Name of the first picture in the picture picture matching task

Distribution

Distribution of values for Picture1

95832 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
Picture1	Name of the first picture in the picture picture matching task	character	95832	0.4998591	48	0	8	22	0

Picture2

Name of the secon picture in the picture picture matching task

Distribution

Distribution of values for Picture2

95832 missing values.

Summary statistics

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
Picture2	Name of the secon picture in the picture picture matching task	character	95832	0.4998591	48	0	8	22	0

Missingness report

Codebook table

JSON-LD metadata

The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.

{
  "name": "Object Orientation across languages",
  "description": "This dataset includes the raw trial data from the PSA002: Object Orientation Across Languages Study. \n\nMental simulation theories of language comprehension propose that people automatically create mental representations of objects mentioned in sentences. Representation is often measured with the sentence-picture verification task, in which participants first read a sentence implying the shape/size/color/object orientation and, on the following screen, a picture of an object. Participants then verify if the pictured object either matched or mismatched the implied visual information mentioned in the sentence. Previous studies indicated the match advantages of shapes, but findings concerning object orientation were mixed across languages. This registered report describes our investigation of the match advantage of object orientation across 18 languages, which was undertaken by multiple laboratories\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n|name                |label                                                                                                    | n_missing|\n|:-------------------|:--------------------------------------------------------------------------------------------------------|---------:|\n|PSA_ID              |Lab identification code                                                                                  |         0|\n|subject_nr          |Original, lab assigned subject number                                                                    |         0|\n|LAB_SEED            |Original, lab assigned seed number for randomization                                                     |     67464|\n|datetime            |Date and time of the study                                                                               |         0|\n|logfile             |Original location of the saved log                                                                       |     67416|\n|task_order          |                                                                                                         |    129498|\n|List                |List file for the presentation of the stimuli                                                            |     95778|\n|Match               |Match or Mismatch of the sentence and picture for sentence picture trials. F indicates practical trials. |     95778|\n|Orientation         |Direction of the stimuli picture presented on the screen                                                 |     95778|\n|PList               |List file for the practice stimuli presentation                                                          |    101514|\n|Probe               |Sentence seen in the sentence picture matching task                                                      |     95778|\n|Target              |Object seen in the sentence picture matching task                                                        |     95778|\n|response_time       |Response time to determine if objects or sentence/picture matched                                        |         0|\n|correct             |Indicates if the participant answered the trial correctly                                                |         1|\n|opensesame_codename |Name of the version of open sesame                                                                       |         0|\n|opensesame_version  |Version number of the open sesame used                                                                   |         0|\n|Language            |Language the participant took the study in                                                               |         0|\n|Source              |Online (osweb) versus in person (all others) data source                                                 |         0|\n|Subject             |A unique participant identifier, as duplicates and other repeating trials were fixed                     |         0|\n|PPList              |The stimulus presentation list for picture picture matching task                                         |     95832|\n|Orientation1        |The orientation of the first picture in the picture picture matching task                                |     95832|\n|Orientation2        |The orientation of the second picture in the picture picture matching task                               |     95832|\n|Identical           |If the two orientations of the pictures matched in the picture picture matching task                     |     95832|\n|Picture1            |Name of the first picture in the picture picture matching task                                           |     95832|\n|Picture2            |Name of the secon picture in the picture picture matching task                                           |     95832|\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.9.2).",
  "identifier": "https://osf.io/e428p/",
  "creator": "Erin M. Buchanan",
  "citation": "Chen et al. (2023). Investigating Object Orientation Effects Across 18 Languages. Registered Report.",
  "url": "https://osf.io/e428p/",
  "datePublished": "2023-02-10",
  "temporalCoverage": "2019-2021",
  "spatialCoverage": "Online and in Person",
  "keywords": ["PSA_ID", "subject_nr", "LAB_SEED", "datetime", "logfile", "task_order", "List", "Match", "Orientation", "PList", "Probe", "Target", "response_time", "correct", "opensesame_codename", "opensesame_version", "Language", "Source", "Subject", "PPList", "Orientation1", "Orientation2", "Identical", "Picture1", "Picture2"],
  "@context": "http://schema.org/",
  "@type": "Dataset",
  "variableMeasured": [
    {
      "name": "PSA_ID",
      "description": "Lab identification code",
      "@type": "propertyValue"
    },
    {
      "name": "subject_nr",
      "description": "Original, lab assigned subject number",
      "@type": "propertyValue"
    },
    {
      "name": "LAB_SEED",
      "description": "Original, lab assigned seed number for randomization",
      "@type": "propertyValue"
    },
    {
      "name": "datetime",
      "description": "Date and time of the study",
      "@type": "propertyValue"
    },
    {
      "name": "logfile",
      "description": "Original location of the saved log",
      "@type": "propertyValue"
    },
    {
      "name": "task_order",
      "description": "",
      "@type": "propertyValue"
    },
    {
      "name": "List",
      "description": "List file for the presentation of the stimuli",
      "@type": "propertyValue"
    },
    {
      "name": "Match",
      "description": "Match or Mismatch of the sentence and picture for sentence picture trials. F indicates practical trials.",
      "@type": "propertyValue"
    },
    {
      "name": "Orientation",
      "description": "Direction of the stimuli picture presented on the screen",
      "@type": "propertyValue"
    },
    {
      "name": "PList",
      "description": "List file for the practice stimuli presentation",
      "@type": "propertyValue"
    },
    {
      "name": "Probe",
      "description": "Sentence seen in the sentence picture matching task",
      "@type": "propertyValue"
    },
    {
      "name": "Target",
      "description": "Object seen in the sentence picture matching task",
      "@type": "propertyValue"
    },
    {
      "name": "response_time",
      "description": "Response time to determine if objects or sentence/picture matched",
      "@type": "propertyValue"
    },
    {
      "name": "correct",
      "description": "Indicates if the participant answered the trial correctly",
      "@type": "propertyValue"
    },
    {
      "name": "opensesame_codename",
      "description": "Name of the version of open sesame",
      "@type": "propertyValue"
    },
    {
      "name": "opensesame_version",
      "description": "Version number of the open sesame used",
      "@type": "propertyValue"
    },
    {
      "name": "Language",
      "description": "Language the participant took the study in",
      "@type": "propertyValue"
    },
    {
      "name": "Source",
      "description": "Online (osweb) versus in person (all others) data source",
      "@type": "propertyValue"
    },
    {
      "name": "Subject",
      "description": "A unique participant identifier, as duplicates and other repeating trials were fixed",
      "@type": "propertyValue"
    },
    {
      "name": "PPList",
      "description": "The stimulus presentation list for picture picture matching task",
      "@type": "propertyValue"
    },
    {
      "name": "Orientation1",
      "description": "The orientation of the first picture in the picture picture matching task",
      "@type": "propertyValue"
    },
    {
      "name": "Orientation2",
      "description": "The orientation of the second picture in the picture picture matching task",
      "@type": "propertyValue"
    },
    {
      "name": "Identical",
      "description": "If the two orientations of the pictures matched in the picture picture matching task",
      "@type": "propertyValue"
    },
    {
      "name": "Picture1",
      "description": "Name of the first picture in the picture picture matching task",
      "@type": "propertyValue"
    },
    {
      "name": "Picture2",
      "description": "Name of the secon picture in the picture picture matching task",
      "@type": "propertyValue"
    }
  ]
}`

PSA 002 Data Codebook

Set Options

Prep Data

Create codebook

Metadata

Description

PSA_ID

Distribution

Summary statistics

subject_nr

Distribution

Summary statistics

LAB_SEED

Distribution

Summary statistics

datetime

Distribution

Summary statistics

logfile

Distribution

Summary statistics

task_order

Distribution

Summary statistics

List

Distribution

Summary statistics

Match

Distribution

Summary statistics

Orientation

Distribution

Summary statistics

PList

Distribution

Summary statistics

Probe

Distribution

Summary statistics

Target

Distribution

Summary statistics

response_time

Distribution

Summary statistics

correct

Distribution

Summary statistics

opensesame_codename

Distribution

Summary statistics

opensesame_version

Distribution

Summary statistics

Language

Distribution

Summary statistics

Source

Distribution

Summary statistics

Subject

Distribution

Summary statistics

PPList

Distribution

Summary statistics

Orientation1

Distribution

Summary statistics

Orientation2

Distribution

Summary statistics

Identical

Distribution

Summary statistics

Picture1

Distribution

Summary statistics

Picture2

Distribution